Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Categories of sound events: environmental, mechanical, and human-generated
• Overview of practical applications: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data and Feature Extraction

• Varieties of audio files and supported formats
• Considerations regarding sampling rate, windowing, and frame size
• Extraction of MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation

• Utilizing datasets such as UrbanSound8K, ESC-50, and proprietary collections
• Annotating sound events and defining temporal boundaries
• Techniques for balancing datasets and augmenting audio inputs

Building Audio Classification Models

• Application of convolutional neural networks (CNNs) for audio tasks
• Evaluating model inputs: raw waveforms versus extracted features
• Selection of loss functions, evaluation metrics, and management of overfitting

Event Detection and Temporal Localization

• Strategies for frame-based and segment-based detection
• Refining detections through thresholds and smoothing algorithms
• Visualizing predictions along audio timelines

Advanced Topics and Real-Time Processing

• Applying transfer learning in low-data environments
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio processing and latency optimizations

Project Development and Application Scenarios

• Designing an end-to-end pipeline from ingestion to classification
• Creating proof-of-concept solutions for surveillance, quality control, or monitoring
• Implementing logging, alerting, and integration with dashboards or APIs

Summary and Next Steps